The cover illustrates that the analysis, via machine learning, of near-infrared-fluorescence emissions of carbon-nanotube sensors placed in serum samples can be used to predict ovarian cancer.
Spectral fingerprinting of ovarian cancer in serum samples
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Detection of ovarian cancer via the spectral fingerprinting of quantum-defect-modified carbon nanotubes in serum by machine learning - Nature Biomedical Engineering
Ovarian cancer can be predicted with high sensitivity and specificity via a fingerprint obtained, via machine learning, from near-infrared fluorescence emissions of an array of carbon nanotube sensors in serum samples.
Nature Biomedical Engineering
This journal aspires to become the most prominent publishing venue in biomedical engineering by bringing together the most important advances in the discipline, enhancing their visibility, and providing overviews of the state of the art in each field.